LLM Agents Improve Long-Term Memory with Selective Retention Framework

Large Language Model (LLM) agents often struggle with long-horizon tasks due to memory limitations and the accumulation of irrelevant information. A new framework, TraceRetain, addresses this by implementing a selective memory retention mechanism for external memory. This system evaluates memory entries based on factors like success, age, access frequency, and utility, then prunes less valuable information when memory capacity is reached. Experiments, particularly in environments with simulated noise, demonstrated TraceRetain's effectiveness. While simple environments showed little difference between retention policies, the framework proved crucial under noisy conditions. It maintained high task success and precision, outperforming unbounded memory and basic caching strategies which degraded significantly when faced with irrelevant data. This research highlights that effective memory management, specifically selective retention, is vital for LLM agents to perform robustly in complex, real-world scenarios where information overload and noise are common. It suggests that simply increasing memory capacity is not enough; intelligent filtering is required.